ArticleJournal of cheminformatics2017
Can human experts predict solubility better than computers?
Article in Journal of cheminformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Rethinking the AI Paradigm for Solubility Prediction of Drug‑Like Compounds with Dual-Perspective Modeling and Experimental Validation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Refined ADME Profiles for ATC Drug Classes.Pharmaceutics · 2025Article
- Artificial intelligence algorithm for preoperative prediction of FIGO stage in ovarian cancer based on clinical features integrated 18F-FDG PET/CT metabolic and radiomics features.Journal of cancer research and clinical oncology · 2025Article
- Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility.ACS omega · 2024Article
- Correction: Predicting small molecules solubility on endpoint devices using deep ensemble neural networks.Digital discovery · 2024Article
- Predicting small molecules solubility on endpoint devices using deep ensemble neural networks.Digital discovery · 2024Article
- SolPredictor: Predicting Solubility with Residual Gated Graph Neural Network.International journal of molecular sciences · 2024Article
- Multi-channel GCN ensembled machine learning model for molecular aqueous solubility prediction on a clean dataset.Molecular diversity · 2023Article
- A Perspective on Explanations of Molecular Prediction Models.Journal of chemical theory and computation · 2023Review
- Article
- Revisiting the Use of Quantum Chemical Calculations in LogPMolecules (Basel, Switzerland) · 2023Review
- Human-in-the-loop assisted de novo molecular design.Journal of cheminformatics · 2022Article
- Quantitative evaluation of explainable graph neural networks for molecular property prediction.Patterns (New York, N.Y.) · 2022Article
- Intrinsic Aqueous Solubility: Mechanistically Transparent Data-Driven Modeling of Drug Substances.Pharmaceutics · 2022Article
- Article
- Individual and collective human intelligence in drug design: evaluating the search strategy.Journal of cheminformatics · 2021Article
- Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules.Pharmaceutics · 2021Article
- SolTranNet-A Machine Learning Tool for Fast Aqueous Solubility Prediction.Journal of chemical information and modeling · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Machine learning with physicochemical relationships: solubility prediction in organic solvents and water.Nature communications · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In this study, we design and carry out a survey, asking human experts to predict the aqueous solubility of druglike organic compounds. We investigate whether these experts, drawn largely from the pharmaceutical industry and academia, can match or exceed the predictive power of algorithms. Alongside this, we implement 10 typical machine learning algorithms on the same dataset. The best algorithm, a variety of neural network known as a multi-layer perceptron, gave an RMSE of 0.985 log S units and an R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.